Brea-Net: An Interpretable Dual-Attention Network for Imbalanced Breast Cancer Classification
Yi Liang, Zuqiang Meng
Abstract
Breast cancer is a prevalent disease worldwide, and early diagnosis plays a vital role in improving patient outcomes. Recent advancements in deep learning have shown great potential for accurate and efficient breast cancer classification. However, the existing methods still suffer from low accuracy and lack of interpretability. To overcome these limitations, we propose a novel and interpretable network to improve the performance of breast cancer classification tasks. By employing a dual-attention module called Convolutional Block Attention Module (CBAM) and a flexible and efficient classifier named Convolutional Multi-Layer Perceptron (ConvMLP), our model is able to effectively capture and exploit the discriminative spatial and channel features within histopathological images and learn complex patterns and relationships between features, leading to improved classification performance. The proposed model outperforms previous state-of-the-art works in terms of accuracy, precision, recall, and F1-score, yielding the highest accuracy of binary and eight-class classification on the BreaKHis dataset. Further, the generalization ability of our proposed network is tested on a different dataset called ICIAR 2018, scoring outstanding accuracy of 95.5%.